datasets:
- ehartford/dolphin
- jondurbin/airoboros-2.2.1
- ehartford/dolphin-coder
- teknium/openhermes
- ise-uiuc/Magicoder-OSS-Instruct-75K
- ise-uiuc/Magicoder-Evol-Instruct-110K
- LDJnr/Capybara
language:
- en
license: apache-2.0
This is pruned down version of cognitivecomputations/dolphin-2.6-mistral-7b-dpo from 7.24B params to 5.93B params (~ 82%).
Steps to replicate:
Use laserQlora.ipynb from cognitivecomputations/laserRMT to determine which layers should be eliminated.
Replace model_name = "mistralai/Mistral-7B-v0.1" with model_name = "cognitivecomputations/dolphin-2.6-mistral-7b-dpo".
I also ran the script only for self_attn.v_proj (so change the script to layer_types=["self_attn.v_proj"])
Order by snr descending and eliminate top layers using mergekit. The threshold for elimination is up to you, depeding on how many layers you want removed. I decided to remove 6 layers (indexes: 3, 5, 16, 18, 19, 24 )
Here is the mergekit config:
slices:
- sources:
- model: "cognitivecomputations/dolphin-2.6-mistral-7b-dpo"
layer_range: [0, 3]
- sources:
- model: "cognitivecomputations/dolphin-2.6-mistral-7b-dpo"
layer_range: [4, 5]
- sources:
- model: "cognitivecomputations/dolphin-2.6-mistral-7b-dpo"
layer_range: [6, 16]
- sources:
- model: "cognitivecomputations/dolphin-2.6-mistral-7b-dpo"
layer_range: [17, 18]
- sources:
- model: "cognitivecomputations/dolphin-2.6-mistral-7b-dpo"
layer_range: [20, 24]
- sources:
- model: "cognitivecomputations/dolphin-2.6-mistral-7b-dpo"
layer_range: [25, 32]
merge_method: passthrough
dtype: bfloat16
The model outputted by mergekit with this configuration is this model (dolphin-2.6-mistral-7b-dpo-5.93B).